2021:Labeling Behavior (Behavior)

2021	This article is in development for the upcoming version of Grooper, Grooper 2021. Labeling Behavior is a new Content Type Behavior option in 2021. This information is incomplete and/or may change by the time of release.

The Labeling Behavior is a Content Type Behavior designed to collect and utilize a document's field labels in a variety of ways. This includes functionality for classification and data extraction.

The Labeling Behavior functionality allows Grooper users to quickly onboard new Document Types for structured and semi-structured forms, utilizing labels as a thumbprint for classification and data extraction purposes. Once the Labeling Behavior is enabled, labels are identified and collected using the "Labels" tab of Document Types. These "Label Sets" can then be used for the following purposes:

• Document classification - Using the Labelset-Based Classification Method
• Field based data extraction - Using the Labeled Value Extractor Type
• Tabular data extraction - Using a Data Table object's Tabular Layout Extract Method
• Sectional data extraction - Using a Data Section object's Transaction Detection Extract Method

About

Labels serve an important function on documents. They give the reader critical context to understand where data is located and what it means. How do you know the difference between the date on an invoice document indicating when the invoice was sent and the date indicating when you should pay the invoice? It's the labels. The labels are what distinguishes one type of date from another. For example, "Invoice Date" for the date the invoice was sent and "Due Date" for the date you need to pay by.

Labels can be a way of classifying documents as well. What does one individual label tell you about a document? Well, maybe not much. However, if you take them all together, they can tell you quite a bit about the kind of document you're looking at. For example, a W-4 employee withholding form is going to use different labels than an employee healthcare enrollment form. These are two very different documents collecting very different information. The labels used to collect this information are thus different as well.

Furthermore, you can even tell the difference between two very closely related documents using labels as well. For example, two different invoices from two different vendors may share some similarity in the labels they use to detail information. But there will be some differences as well. These differences can be useful identifiers to distinguish one from the other. Put all together, labels can act as a thumbprint Grooper can use to classify a document as one Document Type or another.

Even though these two invoices share some labels (highlighted in blue), there are others that are unique to each one (highlighted in yellow). This awareness of how one kind of invoice from one vendor uses labels differently from another can give you a method of classifying these documents using their label sets.

The Labeling Behavior is built on these concepts, collecting and utilizing labels for Document Types in a Content Model for classification and data extraction purposes.

As a Behavior, the Labeling Behavior is enabled on a Content Type object in Grooper. ⚠ While you can enable Labeling Behavior on any Content Type, in almost all cases, you will want to enable this Behavior on the Content Model. Here, we have selected a Content Model in the Node Tree. To add a Behavior, select the Behaviors property and press the ellipsis button at the end. This will bring up a dialogue window to add various behaviors to the Content Model, including the Labeling Behavior Add the Labeling Behavior using the "Add" button. Select Labeling Behavior from the listed options.
Once added, you will see a Labeling Behavior item added to the Behaviors list. Selecting the Labeling Behavior in the list, you will see property configuration options in the right panel. The configuration options in the property panel pertain to fuzzy matching collected labels as well as constrained and vertical wrapping capabilities to target stacked labels. By default, Grooper presumes you will want to use some fuzzy matching and enable constrained and vertical wrapping. These defaults work well for most use cases. However, you can adjust these properties here as needed. Press the "OK" button to finish adding the Labeling Behavior and exit this window.
Once the Labeling Behavior is enabled, the next big step is collecting label sets for the various Document Types in your Content Model. With the Labeling Behavior enabled, you will now see a "Labels" tab present for the Content Model. This tab is also now present for each individual Document Type as well. Label sets are collected in this tab for each Document Type in the Content Model. Each Document Type has its own set of labels used to define information on the document. For example, the "Factura" Document Type in this Content Model uses the label "PO Number" to call out the purchase order number on this invoice document. A different Document Type, corresponding to a different invoice format, might use a different label such as "Purchase Order Number" or "PO #". Ultimately, this is the data we want to collect using the Content Model's Data Model. We use the "Labels" tab to collect labels corresponding to the various Data Elements (Data Fields, Data Tables, and Data Sections) of the Data Model. This provides a user interface to enter a label identifying the value you wish to collect for the Data Elements. For example, the label "PO Number" identifies the purchase order number for this invoice. Therefore, the label "PO Number" is collected for the "Purchase Order Number" Data Field in the Data Model. For more information on collecting label sets for the Document Types in your Content Model see the How To section of this article.
Once label sets are collected for each Document Type, they can be used for classification and data extraction purposes. For example, labels were used in this case to: Classify the document, assinging it the "Factura" Document Type. Extract all the Data Fields seen here, collecting field based data from the document. Extract the "Line Items" Data Table, collecting the tabular data seen here. For more information on how to use labels for these purposes, see the how to section of this article.

How To

Collect Label Sets

Navigate to the Labels UICollect Field LabelsCollect Table and Column LabelsAuto Map LabelsCollect Custom Labels

Navigate to the Labels UI

Collecting labels for the Document Types in your Content Model will be the first thing you want to do after enabling the Labeling Behavior. Labels for each Data Element in the Document Type's Data Model are defined using the "Labels" tab of the Content Model. Navigate to the "Labels" tab of the Content Model. With a Batch selected in the "Batch Selector" window panel, select a document folder. Press the "Set Type..." button to set the Document Type whose labels you wish to collect. This will bring up the "Set Content Type" window. From this window, select the Document Type for the selected document folder whose labels you wish to collect. In this case, this document is an invoice from "Factura Technology Corp". We have selected the "Factura" Document Type. Press "OK" to finish. FYI If you haven't added a Document Type for the selected document folder yet, you can use the "Create Type" button instead to both create a new Document Type and set it.

Upon setting the Document Type the document folder is assigned the selected Document Type Or in other words, this document is now classified as a "Factura" document. Upon setting a Document Type, that Document Type's Data Model and its child Data Elements will appear in the label collection UI. Labels are primarily collected as they correspond to Data Elements in a Data Model. However, we will see how to add custom labels that don't correlate to a Data Element as well by the end of this tutorial. Custom labels are often used as additional features for document classification.

Collect Field Labels

Now that this document has been classified (assigned a Document Type from our Content Model), we can collect labels for its Document Type. This can be done in one of two ways:

1. Lassoing text in the "Document Viewer"
2. Typing them in manually.

‼	Going forward, this tutorial presumes you have obtained machine readable text from these documents, either OCR'd text or native text, via the Recognize activity.

Generally the quickest way is by simply lassoing the label in the "Document Viewer". Select the Data Element whose label you wish to collect. Here, we are selecting the "Invoice Number" Data Field. Press the "Select Region" button. With your cursor, lasso around the text label on the document.
Upon lassoing the label in the Document Viewer, the OCR'd or native text behind the selected region will be used to populate the Data Element's label. At this point, the label for the "Invoice Number" Data Field is now "Invoice Number" because that's the text data we selected. Whatever text characters you lasso with your cursor will be assigned as the label. Notice this label also now appears in the "Header" tab below. That's because we had the Header tab selected when we lassoed the label. The text collected here ("Invoice Number") is the Header label for the "Invoice Number" Data Field. We'll talk about the difference between Header, Footer, and Static labels later. This will be important when using labels for data extraction purposes.
If you choose, you may also manually enter a label for a Data Element by simply typing it into the text box. Here we've selected the "Purchase Order Number" Data Field and entered "PO Number". This will correspond to the label "PO Number" on the document itself. ⚠ Whether lassoing the text using the Document Viewer or manually typing into the textbox, you may collect a maximum of one Header label and one Footer label (and one Static label where avaialable) per Data Element per Document Type.
Upon entering the label into the text box, just you'll see the label in the Header tab, just like we saw when we collected a label by lassoing the text on the Document Viewer. Notice as well, there is a green checkmark next to the "Header" tab (and the box below is highlighted green). This means the text label is matching something on the document. If it did not, you would see a red "X" next to the Header tab and the box below would be highlighted red. Also note, since this label is being returned on this document, we can verify it in the Document Viewer. The selected Data Field ("Purchase Order Number") and it's text label are highlighted green on the document, indicating 1) it was successfully located on the document and 2) where it was located.
Continue lassoing or manually entering labels until all are collected. Next, we will focus on collecting labels from tables and table columns (the Data Table and Data Column elements in a Data Model). The process is essentially the same, but bears some extra explanation.

Collect Table and Column Labels

Table and column labels can be used for tabular data extraction as well, setting a Data Table object to use the Tabular Layout Extract Method. When collecting labels for this method of table extraction, keep in mind you must collect the individual column headers, and may optionally collect both the full row of column header labels as well. While it is optional, it is generally regarded as best practice to capture the full row of column header labels. This will generally increase the accuracy of your column label extraction. We will do both in this tutorial. We will collect the full row of column header labels for the Data Table object's label. We will collect each individual column header label for each individual Data Column object's label. This may seem like you are duplicating your efforts but it is often critical to do both in order for the Tabular Layout Extract Method to map the table's structure and ultimately collect the table's data. In particular if you are dealing with OCR text data containing inaccurate character recognition data, establishing the full header row for the table will boost the fuzzy matching capabilities of the Labeling Behavior.
To collect the Data Table's label, select the Data Table object in the Labels tab. Here, we've selected the Data Table named "Line Items". Lasso the entire header row for the table. You may notice there are more columns on this table than we are collecting. As it is on the document, the table has six columns. But we're only collecting four, the "Quantity", "Description", "Unit Price", and "Line Total" Data Columns. Generally, you should collect the whole row of column headers, even if there are extra columns whose data you are not collecting.
Next, collect each child Data Column's header label. Here, we've selected the "Quantity" Data Column. Lasso the individual column header for the selected Data Column. Here, the stacked label, "Qty. Ord.".
Continue collecting labels for the remaining Data Columns. We have four Data Columns for this Data Table. Therefore, we collect four header labels from the document.

Auto Map Labels

As you add labels for each Document Type, you may find some documents have labels in common. For example, there are only so many ways to label an invoice number. It might be "Invoice Number", "Invoice No", "Invoice #" or even just "Invoice". Some invoices are going to use one label, others another. When collecting labels for multiple Document Types you can use the "Auto Map" feature to automatically add labels you've previously collected on another Document Type. So far, we've only collected labels for one Document Type, the "Factura" Document Type. Now, we're collecting labels for the "Lasku" Document Type. Press the "Auto Map" button to automatically assign previously collected labels,
Grooper will search the document's text for labels matching those previously collected on other Document Types. For example, we collected the label "Remit To:" for the "Remit Address" Data Field for the "Factura" Document Type. The "Auto Map" feature found a match for this label on the document and assigned the "Lasku" Document Type's "Remit Address" Data Field the same label. If a match is not found, the Data Element's label is left blank. For example, the label for the "Invoice Amount" Data Field for the "Factura" Document Type was "Amount due". This label was nowhere to be found on this document. The invoice amount is labeled "Total" on the "Lasku" documents. So, the label is left blank for you to collect. As you keep collecting labels for more and more Document Types, the Auto Map feature will pick up more and more labels, allowing you to quickly onboard new Document Types.
Be aware, you may still need to validate the auto mapped values and make adjustments. For example, the label "Date" is very generic. This label does actually correspond to the invoice date on the "Lasku" Document Type in this case. However, that could label some other date on another Document Type. Even on this document, the label "Date" is returning the "Date" portion of "Ship Date" and other instances where "Date" is found in the text. As a side note, there are ways to make simple labels like "Date" more specific to the data they pertain to using "Custom Labels". More on that in the next tab. You can also make minor adjustments to the mapped labels. The mapped label for the "Purchase Order Number" Data Field was "PO Number" (as it was collected for the "Factura" Document Type), but it is more specifically "PO Number:" on the "Lasku" documents. We can just add the colon at the end of the label manually.

Collect Custom Labels

It's important to keep in mind labels are collected for corresponding Data Elements in a Data Model. You collect one label per Data Element (Data Field, Data Section, Data Table or Data Column). What if you want to collect a label that is distinct from a Data Element, one that doesn't necessarily have to do with a value collected by your Data Model? And why would you even want to?

That's what "Custom Labels" are for. Custom labels serve two primary functions:

1. Providing additional labels for classification purposes.
2. Providing context labels when a Data Element's label matches multiple points on a document

Custom Labels may only be added to Data Model, Data Section or Data Table objects' labels. Put another way, any Data Element in the Data Model's hierarchy that can have child Data Elements can have custom labels. When used for classification purposes, custom labels are typically added to the Data Model itself. First select the Data Element in the Data Model's hierarchy to which you wish to add the label. In our case, we're selecting the Data Model itself. Right-click either the "Header" or "Footer" tab. Press the "Add Custom Label..." button. The following dialogue box will appear. You may enter a name for the custom label, or use the default "Custom ##" naming convention. Press the "OK" button when finished.
This will add a new label tab, named whatever you named it in the previous step. Here, we kept with the default "Custom 01" name. Notice the red "X" next to the name "Custom 01" as well. This indicates the label is not matching anything on the document. Currently the label is "Custom 01", which doesn't appear anywhere on the document. We need to change that by collecting a new label. Collect the custom label by either lassoing the text using the Document Viewer or manually typing in the label. For example, the word "Invoice" might be a useful label for classification purposes. This label isn't used to collect anything in our Data Model, but might be helpful to identify this and other invoices from the Factura Technology Corp as "Factura" Document Types. Collecting the label "Invoice" as a Custom Label will allow us to use it as a feature of this Document Type for classification.
You may add more Custom Labels to the selected Data Element by repeating the process described above. Right-click any of the label tabs. Add a new label with the "Add Custom Label..." button.

Custom Labels as Context Labels

Some labels are more specific than others. The label "Invoice Date" is more specific than the label "Date". If you see the label "Invoice Date" you know the date you're looking at is the date the invoice was generated. The label "Date" may refer to the invoice's generation date or it could be part of another label like "Due Date". However, some invoice formats will label the invoice date as simply "Date". For example, the label "Date" on this "Factura" Document Type does indeed correspond to the invoice date for the "Invoice Date" Data Field. However, this label pops up as part of other labels too, such as the "Date" in "Due Date" or "Order Date". This can present a challenge for data extraction. The possibilities for false-positive results tend to crop up the more generic the label used to identify a desired value. There are three separate date values identified by the word "Date" (in full or in part) on this document.

This is the second reason Custom Labels are typically added for a Document Type, to provide extra context for generic labels, especially when they produce multiple results on a document, leading to false-positive data extraction.

There are two steps to adding and using a Custom Label for this purpose:

1. Add the Custom Label.
2. Marry the Custom Label with the Data Element's label.

We will refer to this type of a Custom Label as a "Context Label" from here out.

The only "trick" to this is adding the Context Label to the appropriate level of the Data Model's hierarchy. Remember, a Custom Label may only be added to a Data Model, Data Section or Data Table object. We cannot add a Custom Label to a Data Field, such as the "Invoice Number" Data Field. To add a Context Label a Data Field can use, we must add the Custom Label to its direct parent Data Element. In the case of the "Invoice Date" Data Field its direct parent Data Element is the Data Model itself. Right-click the "Header" or "Footer" tab and select "Add Custom Label..." to add the Custom Label.
The Custom Label we added was "Date Page". This will provide the simple label "Date" some extra context. Which of the three results for the label "Date" do we want to accept? The one falling within this zone.
Now that we've added the label, we need to marry the Custom Label with the Data Field its giving extra context to. This is done with the Parent property of a Data Field label. In our case, the Custom Label provides extra context for the "Invoice Date" Data Field's label. We've selected the "Invoice Date" Data Field. Select the Parent property. Note: This property is only present for Data Field and Data Column labels. Using the drop down list, select the Custom Label you wish to use for the Context Label.
Notice with this Context Label added... ...We only return a single result for the "Invoice Date" Data Field's label "Date". This is the label we want to associate with this Data Field. The other two results do not fall within the Context Label, and are no longer returned.

Use Label Sets for Field Based Extraction

Using the Labeled Value Extractor Type with Label Sets

Intro to the Labeled Value ExtractorLabel Sets and Labeled ValueBest Practice Considerations

Intro to The Labeled Value Extractor

For most static field based extraction, the Labeling Behavior leverages the Labeled Value Extractor Type. Let's first briefly examine how Labeled Value works outside of the Labeling Behavior functionality.

As the name implies, Labeled Value extractor is designed to return labeled values. A common feature of structured forms is to divide information across a series of fields. But it's not as if you just have a bunch of data randomly strewn throughout the document. Typically, the field's value will be identified by some kind of label. These labels provide the critical context to what the data refers to.

Labeled Value relies on the spatial relationship between the label and the value. Most often labels and their corresponding values are aligned in one of two ways.

1. The value will be to the right of the label.
2. The value will be below the label.

Labeled Value uses two extractors itself, one to find the label and another for the value. If the two extractors results are aligned horizontally or vertically within a certain amount of space (according to how the Labeled Value extractor is configured), the value's result is returned.

For example, we could configure this "Invoice Number" Data Field to utilize the Labeled Value extractor to return the invoice number on the document. Keep in mind this is the "hard" way of doing things. As we will see, the Labeling Behavior will make this process easier. We've set the Value Extractor to Labeled Value The label is returned by the Label Extractor Here, set to a Pattern Match extractor using the regex pattern Invoice Number The value is returned by the Value Extractor Here, set to a Pattern Match extractor using the regex pattern [A-Z]{2}[0-9]{6} The Maximum Distance property is used to determine alignment relationship between the label and the value as well as the maximum distance between the label and value. The default settings are used here, indicating the value can be aligned horizontally, up to 2 inches from the right of the label, or it can be aligned vertically, up to 2 inches below the label. Upon execution, the Label Extractor first finds the label, then looks to see if anything matching the Value Extractor is located according to its layout configuration. Sure enough, there is a result, "IN165798". The Value Extractor's result is collected for the Data Field upon running the Extract activity.

However, the Labeled Value extractor's set up is a little different when combining it with the Labeling Behavior. The end result is a simpler configuration, utilizing collected labels for the Label Extractor.

Label Sets and Labeled Value

Since this Content Model utilizes the Labeling Behavior, at least part of the setup described in the previous tab was unnecessary. If you've collected a label for the Data Field and that Data Field's Value Extractor is set to Labeled Value, there is no need to configure a Label Extractor. Instead, Grooper will pass through the collected label to the Labeled Value extractor. For example, we've already collected a label for the "Invoice Number" Data Field for the "Factura" Document Type. The label Invoice Number is returned on the document for the label identifying the document's invoice number.

With the label collected, the set up for this "Invoice Number" Data Field will be much simpler. Notice the Value Extractor has been set to Labeled Value. The Label Extractor and Value Extractor are unconfigured (or "blank"). However, upon testing extraction, the invoice number is collected. All that was required, in this case was to collect the label and set the Data Field's Value Extractor property to Labeled Value. Magic! Not magic. Label sets. With Labeling Behavior enabled and a label collected for the "Invoice Number" Data Field, the Labeled Value extractor's Label Extractor looks for a match for the collected label. In this case Invoice Number. Furthermore, with Labeling Behavior enabled and a collected label utilized as the Label Extractor, the Labeled Value extractor's Value Extractor will still return a value even if left unconfigured. It will look for the nearest simple segment according to the layout settings (the Maximum Distance and Maximum Noise property). The result "IN165796" is indeed the nearest simple segment and the desired result. So, there is technically nothing else we need to do. However, situations are rarely this simple and straightforward. There are some other considerations we should keep in mind.

⚠	While you can get a result without configuring the Labeled Value extractor's Value Extractor, that doesn't mean you should. It is considered best practice to always configure the Value Extractor.

Best Practice Considerations

While you can get a result without configuring the Labeled Value extractor's Value Extractor, that doesn't mean you should. It is considered best practice to always configure the Value Extractor.

So, why is it considered best practice to do so. The short answer is to increase the accuracy of your data extraction. A simple segment could be anything. If you know the data you're trying to extract has a certain pattern to it, you should target that data according to its pattern. Dates, for example, follow a few different patterns. Maybe it's "07/20/1969" or "07-20-69" or "July 20, 1969", but you know it's a date because it has a specific syntax or pattern to it. To increase the accuracy of your extraction, you should configure the Value Reader with an extractor that returns the kind of data you're attempting to return.

We can see fairly quickly why leaving the Labeled Value extractor's Value Extractor unconfigured is not ideal. All the Data Fields in this Data Section have collected labels and are using the Labeled Value extractor. Except the "Vendor Name" Data Field. Ignore this Data Field for the time being. We only get a few accurate results. Without its Value Extractor configured, the Labeled Value extractor is going to grab whatever segment it can get. While it can be what you want, it is not necessarily what you want. The Value Extractor will allow you to target more specifically what you want to return. Furthermore, while the "Sales Tax" and "Invoice Amount" results may look accurate, they too are not. There are some OCR errors. The extracted segments "0,00" and "54.594.00" should be returned as "0.00" and "54,594.00". The Value Extractor will also allow you to utilize Fuzzy RegEx, Lexicon lookups, output formatting, Data Type Collation methods and other extractor functionalities to manipulate, format, and filter results. For example, the "Date" Data Field returns the segment "Page" to the right of the label Date where it should be returning the date below it, "Feb 26, 2014". If we were instead to configure the Labeled Value extractor's Value Extractor to only return dates, we'd get the more specific result we want and not the generic segment we don't. FYI: When the Value Extractor property is left unconfigured in this manner, the Labeled Value extractor follows a "horizontal then vertical" order of operations. If both a Right Maximum Distance and a Bottom Maximum Distance are configured, it will look for results to the right of the label (aligned horizontally) before looking for results below the label (aligned vertically).
If we reconfigure this "Invoice Date" Data Field slightly we will get a much more accurate result. We've kept the Data Field's Value Extractor set to Labeled Value. The only thing we've changed is we've set the Labeled Value extractor's Value Extractor to a Reference extractor pointing to a Data Type returning dates. Upon testing extraction, we can see now the Data Field collects the value we want, the invoice's date "02/26/2014" By configuring the Labeled Value extractor's Value Extractor, it's no longer looking for just simple segments next to the label. So, the word "Page" is no longer returned. Instead, it's looking for results matching the Value Extractor's results. This increases the specificity of what the Labeled Value returns. Increased specificity yields increased accuracy.
Configuring the Labeled Value extractor's Value Extractor also gives you the myriad of functionalities available to extractors. For example, Fuzzy RegEx is one of the main ways Grooper gets around poor OCR data at the time of extraction. When the text data is just a couple characters off of the extractor's regex pattern, Fuzzy RegEx can not only match the imperfect data but "swap" the wrong characters for the right ones, effectively cleansing your result. Take the "Invoice Amount" Data Field for example. Here, the Data Field's Value Extractor is set to Labeled Value. And, the Labeled Value extractor's Value Extractor is left unconfigured. The Labeled Value extractor first locates the collected label Amount Due and without a configured Value Extractor returns the nearest text segment (according to the Maximum Distance settings). This is almost the result we want. It's the "right" result in that, yes, that is the text segment that corresponds to the invoice amount due for this invoice. But it's very much the wrong result in that the OCR text data is inaccurate. "54.954.00" is not a valid currency value. It should be "54,954.00" with the first period being a comma.
However, that's just a single character off from being the right result. We could build an extractor to return currency values looking to make fuzzy swaps like this, both matching text that is slightly off and reformatting the result to match a valid currency format. If we used that extractor as the Labeled Value extractor's Value Extractor it would not only find the segment but also reformat the result, swapping the mis-OCR'd period for what it should be, a comma. And we've done just that. Here, we've set the Labeled Value extractor's Value Extractor to reference a Data Type returning fuzzy matched currency values. The Value Extractor matches the text we want, below the label Amount Due And since the referenced extractor uses Fuzzy RegEx the returned result is now a valid currency value. The result is now "54,594.00" instead of "54.594.00". The first period was swapped for a comma.

Additional Considerations When Using Labeled Value with Label Sets

Custom Labels to Exclude ResultsMaximum Noise

Custom Labels to Exclude Results

Continuing from the tutorial above's discussion of an unconfigured Labeled Value Value Extractor, let's examine the results of the "Purchase Order Number" Data Field. We've selected the "Purchase Order Number" Data Field in the Node Tree. The Data Field's Value Extractor property is set to Labeled Value. It currently does not have the Labeled Value extractor's Value Extractor configured. Left unconfigured, we get an undesirable result, a rather large text segment "Order Date Customer No. Salesperson Order No. Ship Via". This is obviously not what we want. We want the purchase order number listed below it. Ultimately, we will follow best practice and configure the Labeled Value extractor's Value Extractor property. However, before we do, this gives us an opportunity to demonstrate some additional functionality of the Labeling Behavior. This data "Order Date Customer No. Salesperson Order No. Ship Via" is itself comprised of labels pointing to various values on the document. Even though we haven't set up Data Fields in this Data Model to capture the values they point to, we know this is data we don't want. In general, you don't want to use Grooper to extract labels, you want to extract values.

What's happening here is Grooper is returning all the text on this single line until a collected label in this Document Type's label set is located. In this case, the label Terms was collected for the "Payment Terms" Data Field. None of the text between the label PO Number and the label Terms have been collected in the label set. So, the Labeled Value extractor returns all the text to the right of the "PO Number" Data Field's label (PO Number) and the next encountered label (Terms), resulting in "Order Date Customer No. Salesperson Order Number Ship Via".

⚠

This is very specific functionality to the Labeled Value extractor and its interaction with label sets. It will only behave this way if you: Are using the Labeling Behavior and the Data Field's Value Extractor is set to Labeled Value. Have collected other labels on the same line as the Data Field's label. Have not configured the Labeled Value extractor's Value Extractor.

This may be clearer if we add a Custom Label to the label set. Here's we've added a Custom Label Salesperson to the "Purchase Order Number" Data Field's parent Data Section's labels. If you require information on how to add a Custom Label, see the tutorial above on collecting labels. Be aware, the Custom Label must be added to the Data Field's parent Data Element's labels in order for this to work. This will be either a Data Section if it is a child of a Data Section or the Data Model itself if it is not. In this case, the "Purchase Order Number" is a child of the "Static Fields" Data Section. This is why we added the Custom Label to the Data Section's labels and not the Data Model. Now we have both a Salesperson label and a Terms label for this Document Type's label set.
Now, examine the difference in the "Purchase Order Number" Data Field's extraction result. It stops at the Custom Label we added, Salesperson Ultimately, returning everything between the Data Field's label PO Number and the next label to the right Salesperson In other words, "Order Date Customer No." FYI Keep in mind this is very specific functionality to the Labeled Value extractor and its interaction with label sets. It will only behave this way if you: Are using the Labeling Behavior and the Data Field's Value Extractor is set to Labeled Value. Have collected other labels on the same line as the Data Field's label. Have not configured the Labeled Value extractor's Value Extractor.
If we were to go one step further and add a Order Date Custom Label, we wouldn't get any result returned at all! There is no text between the Data Field's label and another label in the label set, the Labeled Value Extractor will return absolutely nothing at all. FYI One last time, for emphasis... Keep in mind this is very specific functionality to the Labeled Value extractor and its interaction with label sets. It will only behave this way if you: Are using the Labeling Behavior and the Data Field's Value Extractor is set to Labeled Value. Have collected other labels on the same line as the Data Field's label. Have not configured the Labeled Value extractor's Value Extractor.
HOWEVER, this was not the right solution for this problem. This was only an educational exercise to make you aware of how labels in a label set interact with the Labeled Value extractor when its Value Extractor is left unconfigured. We should have followed our best practice advice and configured the Labeled Value extractor's Value Extractor. We did not really have to go through the trouble of adding a bunch of Custom Labels. With the Labeled Value extractor's Value Extractor configured, it's going to ignore this whole business of finding a nearby segment or returning text on a line up to the next label in a label set and more specifically return the data you want to target. Here, we have the Labeled Value extractor's Value Extractor configured to reference a Data Type returning various purchase order number formats. Even without adding all the extra Custom Labels, we get what we want. The "Purchase Order Number" Data Field collects the purchase order number on the document, "PO009845", upon testing extraction.

Maximum Noise

The Maximum Noise property of the Labeled Value extractor controls the maximum number of "noise characters" allowed in the "bounding-region" of a label-value pair. Now, what does that mean? Let's look at an example, using the "Remit Address" Data Field of our example Data Model. We've selected the "Remit Address" Data Field. The Data Field's Value Extractor is set to Labeled Value. The Labeled Value extractor's Label Extractor is left unconfigured. The extractor will use the collected label for this Data Field for each Document Type. The Labeled Value extractor's Value Extractor is configured to reference a Data Type returning all addresses for this document set. We've followed best practice here and assigned a Value Extractor. There's nothing wrong with the referenced Data Type (named "VAL - Address"). It returns the street address and city, state, zip code line for all addresses on these invoices. What we should get upon extracting the document is this: 91 Vahlen Plaza Reston, VA 20191 However, upon testing extraction. No result returns. What gives? It has to do with these "noise characters" mentioned above.
Noise characters are any letters and digits falling within the bounding region defined by a label value. For our example, the bounding region looks like this. The label, highlighted in blue, is established by the Labled Value extractor's Label Extractor result. The value, highlighted in green, is established by the Labeled Value extractor's Value Extractor result. The bounding region, highlighted in yellow, is the smallest rectangle which can enclose both the label and the value.
The noise characters are any letters or numbers within this rectangle other than the label or the value. The highlighted characters in the image would be the noise characters for our example. The Maximum Noise property allows you to configure how many of these non-label and non-value characters should exist in the bounding box. You don't typically expect to find a bunch of text between a label and a value. The Maximum Noise property acts as an additional filter to avoid returning results too far away from the label. Where the Maximum Distance filters out results that are physically a set distance from the label, the Maximum Noise filters results that have lots of text between them and the label. The default being 5, there can be a maximum of 5 letter or number characters between the label and value. However, in our case, we have more than 5. We have 15 ("FacturaTechnolo"). Note: Our case assumes we only want to capture the street address and the city, state, zip line, not the receiver's name.

FYI	Noise characters are only letters and digits. Spaces, punctuation marks, and control characters are NOT considered noise characters, even if present in the bounding region.

Use Label Sets for Tabular Extraction

Label Sets and Tabular Layout1. Collect Labels2. Assign a Data Column's Value Extractor3. Set Extract Method to Tabular Layout and 4. Test

Label Sets and Tabular Layout

Many tables label the columns so the reader knows what the data in that column corresponds to. How do you know the unit price for an item on an invoice? Typically, that item is in a table and one of the columns of that table is labeled "Unit Price" or something similar. Once you read the labels for each column (also called "column headers"), you the reader know where the table begins (below the column headers) and can identify the data in each row (by understanding what the column headers refer to).

This is also the basic idea behind the Tabular Layout Extraction Method. It too utilizes column header labels to "read" tables on documents, or at least as the step number one in modeling the table's structure so that Grooper can extract data from each cell in the table.

Furthermore, using the Tabular Layout method, collected label sets using a Labeling Behavior can also be used to extract data from tables on documents. In this case, the labels collected for the Data Column children of a Data Table are utilized to help model the table's structure.

Once the column header locations are established, the next requirement is a way to understand how many rows are in the table. This is done by configuring at least one Data Column's Value Extractor property. Generally, there is at least one column in a table that is always present for every row in the table. If you can use an extractor to locate that data below its corresponding column header, that gives you a way of finding each row in the table.

And last there are a few other considerations you might need to make. Is every row in the table a single line or are the rows "multiline"? Do you need to clean up the data the Tabular Layout initially extracts for a column by normalizing it with an extractor? Do you need to establish a table "footer" to limit the number of rows extracted?

This tutorial will cover the basic configuration of the Tabular Layout Extraction Method using collected Label Sets and address a few of these considerations.

The basic steps will be as follows: Collect labels. At minimum you must collect a header label for each Data Column child in the Data Table. We will also discus the benefits of collecting label for the full header row. Assign a Value Extractor for at least one Data Column. We always expect to find a quantity for each line item in the invoice. There's always a "Quantity" column. This data is also present on every row. This will provide the information necessary to find each row in the table. We will also discus why you might configure the Value Extractor property on additional Data Columns as well. Set the Data Table object's Extract Method property to Tabular Layout Test to ensure the table's data is collected. In a perfect world, you're done at that point. As you can see in this example, we've populated a table. Data is collected for all four Data Columns for each row on the document. However, the world is rarely perfect. We will discuss some further configuration considerations to help you get the most out of this table extraction method in the "Additional Considerations" section below.

Collect Labels

See the above how to (Collect Label Sets) for a full explanation of how to collect labels for Document Types in a Content Model. The following tutorial will presume you have general familiarity with collecting labels.

As far as strict requirements for collecting labels for tabular data extraction goes, you must at minimum collect a label for each Data Column you wish to extract. For this "Stuff and Things" Document Type, one column header label has been collected for each of the four Data Column children of the "Line Items" Data Table. The label Quantity for the "Quantity" Data Column The label Description for the "Description" Data Column The label Unit Price for the "Unit Price" Data Column The label Total for the "Line Total" Data Column

You may optionally collect a label for the entire row of column header labels. This label is collected for the parent Data Table object's label. The label Quantity Item Serial Number Description Unit Price Total for the "Line Items" Data Table It is generally considered best practice to capture a header row label for the Data Table. But if it's optional, why do it? What is the benefit of this label?

The answer has to do with imperfect OCR text data and Fuzzy RegEx. Fuzzy RegEx provides a way for regular expression patterns to match in Grooper when the text data doesn't strictly match the pattern. The difference between the regex pattern Grooper and the character string "Gro0per" is just off by a single character. An OCR engine misreading an "o" character for a zero is not uncommon by any means, but a standard regex pattern of Grooper will not match the string "Gro0per". The pattern expects there to be an "o" where there is a zero.

Using Fuzzy RegEx instead of regular regex, Grooper will evaluate the difference between the regex pattern and the string. If it's similar enough (if it falls within a percentage similarity threshold) Grooper will return it as a match.

• FYI "similarity" may also be referred to as "confidence" when evaluating (or scoring) fuzzy match results. Grooper is more or less confident the result matches the regex pattern based on the fuzzy regex similarity between the pattern and the imperfect text data. A similarity of 90% and a confidence score of 90% are functionally the same thing (One could argue there is a difference between these two terms when Fuzzy Match Weightings come into play, but that's a whole different topic. And you may encounter Grooper users who use the terms "similarity" and "confidence" interchangeably regardless. Visit the Fuzzy RegEx article if you would like to learn more).

So how does this apply to the Data Table's column header row label? The short answer is it provides a way to increase the accuracy of Data Column column header labels by "boosting" the similarity of the label to imperfect OCR results. For example, examine the collected label for the "Description" Data Column. Notice the label Description is highlighted red. The label doesn't match the text on the document. This is due to imperfect OCR results. The label should read "Description" but OCR made some missteps and recognized that segment as "DescripUon". The "ti" in "Description" were recognized as a capital "U". This means "Description" is two characters different from "Description" or roughly 82% similar. The Labeling Behavior's similarity threshold is set to 90% for this Content Model. 81% is less than 90%. So, the result is thrown out. FYI, this threshold is configured when the Labeling Behavior is added using the Behaviors property of a Content Model. The Label Similarity property is set to 90% by default, but can be adjusted at any time by configuring the Labeling Behavior item in the Behaviors list. As we will see, capturing the full row of column header labels will boost the similarity, allowing the label to match without altering the Label Behavior's fuzzy match settings.
First, notice what's happened when we lassoed the row of column header labels. Some of the labels are off. "oty." should read "Qty." and "DescripUon" should read "Description". It's because that's what's in the document's text. When you lasso a label, it's going to grab whatever OCR text data was generated from the Recognize activity (or native text for digital documents). And, our "Description" Data Field's label still isn't matching. But keep your eye on the birdie.
Notice what happens when we spell-correct the lassoed label, typing "Qty." instead of "oty." and "Description" instead of "DescripUon". Now the label matches. MAGIC! Not magic. Just math. The Data Table's column header row label is much much longer than a single Data Column's column header label. There are just more characters in "Qty. Qty. Item Number Description Unit Price Extended Price\r\nOrd. Shp." than "Description" (70 vs 11). Where the "Description" Data Column's label is roughly 82% similar to the text data (9 out of 11 characters), the "Line Item" Data Table's label, comprised of the whole row of column labels, is roughly 96% similar to the text data (67 out of 70 characters). Utilizing a Data Table label allows you to hijack the whole row's similarity score when a single Data Column's similarity threshold. If the label can be matched as a part of the larger whole, its confidence score goes up much further than by itself. The Data Table's larger label of the full row of column labels gives extra context to the "Description" Data Column's label, providing more information about what is and is not an appropriate match. So why is it considered best practice to capture a label for the Data Table? OCR errors are unpredictable. The set of examples you worked with when architecting this solution may have been fairly clean with good OCR reads. That may not always be the case. Capturing a Data Table label for the column label row will act as a safety net to avoid unforeseen problems in the future.

Assign a Data Column's Value Extractor

Step 1 is done. We've collected labels for the "Line Item" Data Table and its Data Columns for each Document Type in this Content Model. Step 2 is configuring and assigning a Value Extractor for at least one Data Column.

Why is this necessary? Think about what we've done so far. We've collected labels for the Data Columns. Grooper now has a way to figure out where the columns are on the document. But what does it know about the rows?

Rows come under columns. We know that much. So, Grooper at least knows to look for rows underneath the collected Data Column labels. But that's about it. It doesn't know the size of each row. It doesn't know the spacing between the rows. Probably most importantly, it doesn't know how many rows there are. Tables tend to be dynamic. They may have 3 rows on one document and 300 on the next. Grooper needs a way of detecting this.

Indeed, if we were to test extraction with just labels collected, we would not get any result whatsoever. FYI you can test data extraction directly from the Labels UI using the "Test" button. This will create a new "Results" tab, showing you a preview of the results the Extract activity collects from the selected document folder, as defined by its Document Type's Data Model. As you can see, we get no extraction results for the "Line Item" Data Table.
This is why we need a Data Column's Value Extractor property configured, to give the Extract activity an awareness of the rows beneath the column labels. The key thing to keep in mind is this data must be present on every row. You'll want to pick a column whos data is always present for every row, where it would be considered invalid if the information wasn't in that cell for a given row. In our case, we will choose the "Quantity" Data Column. We always expect there to be a quantity listed for the line item on the invoice, even if that quantity is just "1". We will select the "Quantity" Data Column in the Node Tree. We will configure the Value Extractor to return the numerical quantity listed for every line item on every row of the table. We will keep this fairly simple for demonstration purposes, using a Pattern Match extractor.
This is the pattern we will use for the "Quantity" Data Column's Value Extractor. The regex is a fairly simple pattern to match generic quantities. It'll match one to three digits with an optional decimal followed by zero to four digits. And, that must be surrounded by a space character before and after. As you can see, we get two results below the "Quantity" label. We should then get two rows when this table extracts. We get a bunch of other hits as well. This is a very generic extractor matching very generic numerical data. Will this result present a problem? Will we get an extra row for its result? No. That result is above the label collected for the Data Column. The Tabular Layout method presumes rows are below column labels. Any result above them will be ignored. What about results like these? Will this present problem? The short answer is no. This result is misaligned with the "Quantity" Data Column's header. It's too far to the right to be considered "under" it and will be ignored as a candidate to produce a row. That said, when you are building your own Data Column extractors, do pay more attention to results below the column header row. They have the most potential to produce false positive results, producing erroneous rows.

For fairly simple table structures we now have the two things the Tabular Layout method needs to extract data:

1. Collected labels for the Data Column labels (and optionally the whole row of column labels for the Data Table)
2. Configured at least one Data Column with its Value Extractor configured.

Now, all we need to do is tell Data Table object we want to use the Tabular Layout method. We do this by setting its Extract Method property to Tabular Layout.

Set Extract Method to Tabular Layout and Test

A Data Table's extraction method is set using the Extract Method property. To enable the Tabular Layout method, do the following. Select a Data Table object in your Data Model. Here, we've selected the "Line Items" Data Table. Select the Extract Method property. Using the dropdown menu, select Tabular Layout
Now, let's test out what we have and see what we get! For the selected document folder in the "Batch Viewer" window... Press the "Test Extraction" button. Side note: We've seen before we can test extraction using the "Labels" tab of a Content Model or Document Type when Labeling Behavior is enabled. The only real difference is we're testing extraction for the specific Data Element selected in the Node Tree. In this case the "Line Items" Data Model. The "Test" button in the "Labels" tab will test extraction for the entire Data Model and all its component child Data Elements. However, feel free to test extraction at either location. The end result is the same. We're testing to verify extraction results. The results show up in the "Data Element Preview" window. For the Tabular Layout method, the Data Table is populated using primarily two pieces of information. The location and width of the Data Column header labels. This determines the width of the cells for each column. Side note: The width of the column cells is actually determined differently depending on if the table has lines. If the table has lines (as it does in this example) and those lines were previously detected via a Line Detection (or Line Removal) IP Command, the cell width will be expanded to the boundaries of the lines. Table lines give human readers an indicator of where the data "lives" (or is contained). If it's in the box, it belongs to the column. If it's out of the box, it belongs to a different column. The number of rows as determined by the Data Columns whose Value Extractor property is configured. One row is established for each result the Value Extractor returns.
With these pieces of information, the Tabular Layout method can start to determine the table's structure. If you know where the columns are and how big they are, and you know how many rows there are, you pretty much know what the table looks like. This allows Grooper to create data instances for each cell in the table. Once the Tabular Layout method establishes the boundaries of each cell, Grooper "knows" where the table data is located on the page. The text data (either OCR'd text or native digital text obtained from the Recognize activity) is extracted from each cell instance, populating the Data Table and collecting these results when the Extract activity runs. This is for extremely basic configurations, there are some more advanced configuration techniques to either adjust the size of the cell instances and/or extract data for each cell. Some of these will be discussed in the #Additional Considerations section below.

Additional Considerations

Multiline RowsFooter LabelsData Column Value ExtractorsData Element Overrides

Multiline Rows

Footer Labels

Data Column Value Extractors

Data Element Overrides

Additional Information

Include information in this section on the following topics if not able to flesh it out in the About or How To sections. And probably this section will be helpful even if you do talk about it earlier. There's no space in Design Studio to detail this information in a help panel.

Version Differences

2021

The Labeling Behavior is brand new functionality in Grooper version 2021. Prior to this version, its functionality may have been able to be approximated by other objects and their properties (For example, a Data Type using the Key-Value Pair collation is at least in some ways similar to how the Labeled Value Extractor Type works). However, creation of label sets using Document Types and their implementation described above was not available prior to version 2021.